CN113128012A - Disaster guarantee resource calculation method, device, computer device and storage medium - Google Patents
Disaster guarantee resource calculation method, device, computer device and storage medium Download PDFInfo
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Abstract
The invention provides a disaster guarantee resource calculation method, a disaster guarantee resource calculation device, a computer device and a computer storage medium, wherein the method comprises the following steps: acquiring disaster prevention condition data of a site to be evaluated and loss condition data of the site to be evaluated in a disaster scene, wherein items in the disaster prevention condition data comprise environment state information, article state information and personnel number; and inputting the disaster prevention condition data and the loss condition data into a preset calculation model, and outputting disaster guarantee resources required by the to-be-evaluated site in the disaster scene. By the method, the disaster guarantee resources required by the to-be-evaluated place can be obtained more accurately and rapidly.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a disaster guarantee resource calculation method, a disaster guarantee resource calculation device, a computer device and a computer storage medium.
Background
With the idea of disaster prevention getting into full depth, people often prevent possible disasters before a disaster comes, more and more enterprise users often need to create some guarantee accounts and store preset resources in the guarantee accounts as guarantees in the operation process, the stored resources are kept by the cooperators of the enterprises before the disaster happens, and when the disaster happens to the enterprises, the resources in the guarantee accounts are transferred to the accounts of the enterprises according to the preset proportion by the cooperators of the enterprises to serve as the guarantee of the enterprises for resisting the disaster. Therefore, the amount of disaster guarantee resources prestored in the guarantee account is very important, and the existing calculation method for the disaster guarantee resources is low in efficiency and not intelligent.
Disclosure of Invention
In view of the above, there is a need for a disaster-guaranteed resource calculation method, a disaster-guaranteed resource calculation apparatus, a computer apparatus, and a computer storage medium, in which disaster-guaranteed resource calculation is performed in a more efficient and intelligent manner.
A first aspect of the present application provides a disaster assurance resource calculation method, including:
acquiring disaster prevention condition data of a site to be evaluated and loss condition data of the site to be evaluated in a disaster scene, wherein items in the disaster prevention condition data comprise environment state information, article state information and personnel number;
and inputting the disaster prevention condition data and the loss condition data into a preset calculation model, and outputting disaster guarantee resources required by the to-be-evaluated site in the disaster scene.
Preferably, the method for acquiring the data of the damage condition of the place to be evaluated in the disaster scene comprises the following steps:
acquiring disaster prevention condition data of a to-be-evaluated site, performing simulation under different disaster scenes on the to-be-evaluated site through a disaster numerical simulation system, and calculating loss condition data of the to-be-evaluated site under the different disaster scenes.
Preferably, the method further comprises:
simulating the disaster prevention condition data of the site to be evaluated by a disaster prediction simulation system, and judging whether a disaster is caused along with the change of the disaster prevention condition data;
the method for judging whether disasters can be caused comprises the following steps:
dividing the data into a plurality of sections according to the change range of the disaster prevention condition data of each place;
sequentially inputting the disaster prevention condition data of each place into a disaster prediction simulation system according to different variable quantities according to the change rule of the interval;
and if the change of the data can trigger a disaster-causing condition, determining that the data can cause the disaster, wherein the disaster-causing condition comprises temperature, dust content in the air and harmful gas concentration in the air.
Preferably, the method for performing simulation on the to-be-evaluated site in different disaster scenes by using a disaster numerical simulation system and calculating the loss condition data of the to-be-evaluated site in the different disaster scenes includes:
setting the loss proportion of each object in the to-be-evaluated place in each disaster within unit time;
dividing the object according to a preset proportion, wherein each divided area represents the minimum amount of money lost by the object in a disaster within a unit time;
and calculating the loss condition data of the place in each disaster scene according to the loss proportion of the object in each disaster in unit time and the minimum amount lost in each disaster in unit time.
Preferably, the training of the computational model comprises:
acquiring disaster prevention condition data of different places, loss condition data of the places in disaster scenes and disaster guarantee resources required by the places in the disaster scenes, correspondingly storing the disaster prevention condition data of each place, the loss condition data of the places in the disaster scenes and the disaster guarantee resources required by the places in the disaster scenes, and dividing the disaster prevention condition data, the loss condition data and the disaster guarantee data of the places into a training set and a verification set;
establishing a neural network-based calculation model, and training parameters of the calculation model by using the training set, wherein disaster prevention condition data and loss condition data in the training set are used as input data of the model, and the disaster guarantee data is used as output data of the model;
verifying the trained calculation model by using the verification set, and counting according to a verification result to obtain the model prediction accuracy;
judging whether the model prediction accuracy is smaller than a preset threshold value or not;
and if the model prediction accuracy is not smaller than the preset threshold, finishing the training of the calculation model.
Preferably, the method further comprises:
if the model prediction accuracy is smaller than the preset threshold, adjusting the structure of the calculation model, and retraining the adjusted calculation model by using the training set, wherein the structure of the calculation model comprises at least one of the number of convolution kernels, the number of elements in the pooling layer and the number of elements in the full connection layer;
verifying the retrained calculation model by using the verification set, carrying out statistics again according to a verification result to obtain the model prediction accuracy of the adjusted calculation model, and judging whether the prediction accuracy of the adjusted calculation model is smaller than the preset threshold value or not;
if the model prediction accuracy obtained by the re-statistics is not smaller than the preset threshold, finishing the training of the calculation model;
if the model prediction accuracy obtained by the re-statistics is smaller than the preset threshold, repeating the steps of adjusting and training until the model prediction accuracy obtained by the verification of the verification set is not smaller than the preset threshold.
A second aspect of the present application provides a disaster securing resource calculation apparatus, the apparatus comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring disaster prevention condition data of a place to be evaluated and loss condition data of the place in a disaster scene, and items in the disaster prevention condition data comprise environment state information, article state information and personnel number;
and the computing module is used for inputting the disaster prevention condition data and the loss condition data into a preset computing model and outputting disaster guarantee data required by the to-be-evaluated site in the disaster scene.
A third aspect of the application provides a computer arrangement comprising a processor for implementing the disaster assurance resource calculation method as described above when executing a computer program stored in a memory.
A fourth aspect of the present application provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the disaster guaranteed resource calculation method as described above.
According to the disaster guarantee resource calculation method, the disaster guarantee resource calculation device, the computer device and the computer storage medium, disaster prevention condition data of a place to be evaluated and loss condition data of the place in a disaster scene are input into a preset calculation model, disaster guarantee resources required by the place to be evaluated in the disaster scene are output, and the disaster guarantee resources required by the place to be evaluated can be obtained more accurately and rapidly through the method.
Drawings
Fig. 1 is a schematic view of an application environment architecture of a disaster assurance resource calculation method according to an embodiment of the present invention.
Fig. 2 is a flowchart of a disaster assurance resource calculation method according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a disaster insurance resource calculation apparatus according to a third embodiment of the present invention.
Fig. 4 is a schematic diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Example one
Fig. 1 is a schematic view of an application environment architecture of a disaster assurance resource calculation method according to an embodiment of the present invention.
The disaster guarantee resource calculation method is applied to a user terminal 1, and the user terminal 1 and a computer device 2 establish communication connection through a network. The network may be a wired network or a Wireless network, such as radio, Wireless Fidelity (WIFI), cellular, satellite, broadcast, etc. The user terminal 1 is configured to obtain disaster prevention status data of a place to be evaluated and loss status data of the place in a disaster scene, and analyze disaster guarantee resources required by the place to be evaluated in the disaster scene by using a preset calculation model, and the computer device 2 is configured to store the disaster prevention status data of different places, the loss status data of the place in the disaster scene, and the disaster guarantee resources required by the place in the disaster scene.
The user terminal 1 may be an electronic device, such as a personal computer, a tablet computer, or the like, in which disaster support resource calculation method software is installed.
The computer device 2 may be an electronic device, such as a personal computer, a server, or the like, which stores disaster prevention status data of different locations, loss status data of the locations in a disaster scene, and disaster guarantee resources required by the locations in the disaster scene, where the server may be a single server, a server cluster, a cloud server, or the like.
In still another embodiment of the present invention, disaster prevention situation data of the different location, damage situation data of the location in a disaster scene, and disaster guarantee resources required by the location in the disaster scene may be stored in the user terminal 1.
Example two
Fig. 2 is a flowchart illustrating a disaster-guaranteed resource calculation method according to a second embodiment of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
And step S1, acquiring disaster prevention condition data of the place to be evaluated and loss condition data of the place in a disaster scene.
The items in the disaster prevention condition data comprise environment state information, article state information and personnel number. Wherein the environmental status information may include fixed fire protection facility information, mobile fire protection facility information, exhaust facility information, environmental temperature and humidity information, for example: the fire alarm, the smoke alarm, the ceiling fire sprinkler head, the fire detector, the number and the placement positions of the fire hydrant, the number and the positions of the air outlets and the like. The item status message includes the name of the item in the site to be evaluated, and the placement position information of the item, such as the number and placement positions of manufacturing equipment, materials, office computers, furniture, and the like.
In one embodiment, the acquiring manner of the disaster prevention data of the site to be evaluated may include: and receiving information such as the number of evacuated persons, the type and the number of fire-fighting facilities, the number and the positions of air outlets, the type and the number of articles, the placing position and the like of the place to be evaluated, which is input by a user.
In another embodiment, the disaster prevention data acquisition method may further include receiving a plurality of images of a to-be-evaluated place collected by a plurality of cameras, and identifying information such as the number of evacuated persons, the type and number of fire fighting facilities, the number and positions of air outlets, the type and number of articles, and the placement position in the images by using an image identification method.
In one embodiment, the method for acquiring loss condition data of the site in a disaster scene comprises the following steps:
acquiring disaster prevention condition data of a place to be evaluated, simulating the place under different disaster scenes through a disaster numerical simulation system, and calculating loss condition data of the place under the different disaster scenes. The method for calculating the loss condition data of the place under different disaster scenes comprises the steps of setting the loss proportion of each object in the place in unit time, dividing the objects according to the preset proportion, and representing the minimum sum of the loss of the combustible in the fire in unit time by each divided area; and calculating the loss condition data of the place in different disaster scenes according to the loss proportion of the object in unit time and the minimum amount of loss in unit time in the disaster scenes.
For example, a photolithography machine having a value of 81 ten thousand, each having a value of 1 ten thousand, was divided into 81 equal parts, and the burning time of each divided part was 2 minutes. According to the disaster scene of the photoetching machine, inquiring the loss proportion of the photoetching machine in different disasters in unit time. For example, the loss proportion of the lithography machine in a fire scene with a fire sprinkler device and an automatic fire alarm device is 2%, and the loss data of the lithography machine in a fire place with 30 minutes can be calculated to be 15 thousands.
In another embodiment of the present invention, the method for acquiring data of damage conditions of the site in a disaster scene further includes:
acquiring disaster prevention condition data of a place to be evaluated, simulating the disaster prevention condition data of the place through a disaster prediction simulation system, and judging whether a disaster is caused along with the change of the disaster prevention condition data;
the judging method comprises the following steps: dividing the data into a plurality of preset intervals according to the change range of the disaster prevention condition data of each place;
sequentially inputting the disaster prevention condition data of each place into a disaster prediction simulation system according to different variable quantities according to the change rule of the interval;
and if the change of the data can trigger a disaster-causing condition, determining that the data can cause the disaster, wherein the disaster-causing condition comprises temperature, dust content in the air and harmful gas concentration in the air.
For example, it is analyzed whether a change in temperature at a predetermined site will result in a deterioration of the air quality within the site, wherein the deterioration of the air quality is caused by an increase in the concentration of sulfides in the air. In one embodiment, the predetermined location is a chemical product processing workshop. Inputting all articles in the chemical product processing workshop and the exhaust fire-fighting diagram of the workshop into a disaster prediction simulation system, and calculating the concentration of sulfide in the air of the processing workshop through the simulation system when the temperature of the processing workshop changes by 2 degrees. And judging whether the concentration of sulfide in the air changes along with the change of the temperature, if so, the temperature of the preset place is the reason for influencing the air deterioration of the preset place. If the temperature of the preset place is not changed, the temperature of the preset place is not a reason for influencing air deterioration of the preset place. When the air quality of the preset place can be influenced by the change of the temperature, the sulfide concentrations corresponding to different temperatures are correspondingly stored, and the temperature value when the sulfide concentration reaches the concentration influencing the air quality is recorded.
And step S2, inputting the disaster prevention condition data and the loss condition data into a preset calculation model, and outputting disaster guarantee resources required by the site to be evaluated in the disaster scene.
For example, in an embodiment of the present invention, the disaster insurance resource calculation method is applied to a premium calculation system of an insurance company. The disaster prevention condition data includes environmental condition information, article condition information, and personnel condition information in the location to be insured. The damage status data may be a damage status of the to-be-insured place in a fire, and the disaster securing resource is an amount of money to be insured for the to-be-insured place. And inputting the disaster prevention condition data and the loss condition data of the to-be-insured place into a preset calculation model, and outputting the premium amount required by the to-be-insured place in the disaster scene.
The training method of the preset calculation model comprises the following steps:
(1) acquiring disaster prevention condition data of different places, loss condition data of the places in disaster scenes and disaster guarantee resources required by the places in the disaster scenes, correspondingly storing the disaster prevention condition data of each place, the loss condition data of the places in the disaster scenes and the disaster guarantee resources required by the places in the disaster scenes, and dividing the disaster prevention condition data, the loss condition data and the disaster guarantee data of the places into a training set and a verification set.
Disaster guarantee resources required by the site in the disaster scene are disaster guarantee resources historically invested by the site, and the historically invested disaster guarantee resources are stored in the computer device 2.
For example, the computer device 2 is an electronic device for storing user data of an insurance company, and the electronic device stores policy information of a historical insurance application site, the policy information including disaster prevention condition data of the insurance application site, damage condition data of the disaster in which the site is present, which is simulated by a disaster numerical simulation system, and an application amount of the insurance application site. And dividing the data in the historical policy information into a training set and a verification set.
(2) Establishing a neural network-based calculation model, and training parameters of the calculation model by using the training set, wherein disaster prevention condition data and loss condition data in the training set are used as input data of the model, and the disaster guarantee data is used as output data of the model.
The neural network-based computing model comprises various algorithm structures and can comprise a convolutional neural network-based computing model, a genetic algorithm-based neural network, a fuzzy theory-based neural network and the like.
(3) And verifying the trained calculation model by using the verification set, and counting according to a verification result to obtain the model prediction accuracy.
And inputting the disaster prevention condition data in the verification set and the loss condition data of the site in the disaster scene into the calculation model, calculating disaster guarantee resources required by the site in the disaster scene, comparing the calculated disaster guarantee resources with the disaster guarantee resources in the training set, and verifying the prediction accuracy of the model according to the comparison result.
(4) And judging whether the model prediction accuracy is smaller than a preset threshold value or not.
In one embodiment, the prediction accuracy is 95%.
(5) And if the model prediction accuracy is not smaller than the preset threshold, finishing the training of the calculation model.
(6) And if the model prediction accuracy is smaller than the preset threshold, adjusting the structure of the calculation model, and retraining the adjusted calculation model by using the training set, wherein the structure of the calculation model comprises at least one of the number of convolution kernels, the number of elements in the pooling layer and the number of elements in the full connection layer.
(7) And verifying the retrained calculation model by using the verification set, carrying out statistics again according to verification results to obtain the model prediction accuracy of the adjusted calculation model, and judging whether the prediction accuracy of the adjusted calculation model is smaller than the preset threshold value.
(8) And if the model prediction accuracy obtained by the re-statistics is not less than the preset threshold, finishing the training of the calculation model.
(9) If the model prediction accuracy obtained by the re-statistics is smaller than the preset threshold, repeating the steps of adjusting and training until the model prediction accuracy obtained by the verification of the verification set is not smaller than the preset threshold.
The steps in the above-mentioned training method for the calculation model may be changed according to the order of actually required steps, and some steps may be omitted. The training method can be completed on line or off line.
Fig. 2 illustrates the disaster support resource calculation method according to the present invention in detail, and functional modules of a software device for implementing the disaster support resource calculation method and a hardware device architecture for implementing the disaster support resource calculation method are described below with reference to fig. 3 to 4.
It is to be understood that the embodiments are illustrative only and that the scope of the claims is not limited to this configuration.
EXAMPLE III
FIG. 3 is a block diagram of a disaster guaranteed resource computing device according to a preferred embodiment of the present invention.
In some embodiments, the disaster securing resource computing device 10 operates in a computer device. The computer device is connected with a plurality of user terminals through a network. The disaster securing resource computing device 10 may include a plurality of functional modules composed of program code segments. Program codes of respective program segments in the disaster securing resource calculation device 10 may be stored in a memory of the computer device and executed by the at least one processor to implement a disaster securing resource calculation function.
In this embodiment, the disaster guaranteed resource calculation device 10 may be divided into a plurality of functional modules according to the functions to be executed by the device. Referring to fig. 3, the functional modules may include: the device comprises an acquisition module 101 and a calculation module 102. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
The obtaining module 101 is configured to obtain disaster prevention data of a place to be evaluated and loss condition data of the place in a disaster scene.
The items in the disaster prevention condition data comprise environment state information, article state information and personnel number. Wherein the environmental status information may include fixed fire protection facility information, mobile fire protection facility information, exhaust facility information, environmental temperature and humidity information, for example: the fire alarm, the smoke alarm, the ceiling fire sprinkler head, the fire detector, the number and the placement positions of the fire hydrant, the number and the positions of the air outlets and the like. The item status message includes the name of the item in the site to be evaluated, and the placement position information of the item, such as the number and placement positions of manufacturing equipment, materials, office computers, furniture, and the like.
In one embodiment, the acquiring manner of the disaster prevention data of the site to be evaluated may include: and receiving information such as the number of evacuated persons, the type and the number of fire-fighting facilities, the number and the positions of air outlets, the type and the number of articles, the placing position and the like of the place to be evaluated, which is input by a user.
In another embodiment, the disaster prevention data acquisition method may further include receiving a plurality of images of a to-be-evaluated place collected by a plurality of cameras, and identifying information such as the number of evacuated persons, the type and number of fire fighting facilities, the number and positions of air outlets, the type and number of articles, and the placement position in the images by using an image identification method.
In one embodiment, the method for acquiring loss condition data of the site in a disaster scene comprises the following steps:
acquiring disaster prevention condition data of a place to be evaluated, simulating the place under different disaster scenes through a disaster numerical simulation system, and calculating loss condition data of the place under the different disaster scenes. The method for calculating the loss condition data of the place under different disaster scenes comprises the steps of setting the loss proportion of each object in the place in unit time, dividing the objects according to the preset proportion, and representing the minimum sum of the loss of the combustible in the fire in unit time by each divided area; and calculating the loss condition data of the place in different disaster scenes according to the loss proportion of the object in unit time and the minimum amount of loss in unit time in the disaster scenes.
For example, a photolithography machine having a value of 81 ten thousand, each having a value of 1 ten thousand, was divided into 81 equal parts, and the burning time of each divided part was 2 minutes. According to the disaster scene of the photoetching machine, inquiring the loss proportion of the photoetching machine in different disasters in unit time. For example, the loss proportion of the lithography machine in a fire scene with a fire sprinkler device and an automatic fire alarm device is 2%, and the loss data of the lithography machine in a fire place with 30 minutes can be calculated to be 15 thousands.
In another embodiment of the present invention, the method for acquiring data of damage conditions of the site in a disaster scene further includes:
acquiring disaster prevention condition data of a place to be evaluated, simulating the disaster prevention condition data of the place through a disaster prediction simulation system, and judging whether a disaster is caused along with the change of the disaster prevention condition data;
the judging method comprises the following steps: dividing the data into a plurality of preset intervals according to the change range of the disaster prevention condition data of each place;
sequentially inputting the disaster prevention condition data of each place into a disaster prediction simulation system according to different variable quantities according to the change rule of the interval;
and if the change of the data can trigger a disaster-causing condition, determining that the data can cause the disaster, wherein the disaster-causing condition comprises temperature, dust content in the air and harmful gas concentration in the air.
For example, it is analyzed whether a change in temperature at a predetermined site will result in a deterioration of the air quality within the site, wherein the deterioration of the air quality is caused by an increase in the concentration of sulfides in the air. In one embodiment, the predetermined location is a chemical product processing workshop. Inputting all articles in the chemical product processing workshop and the exhaust fire-fighting diagram of the workshop into a disaster prediction simulation system, and calculating the concentration of sulfide in the air of the processing workshop through the simulation system when the temperature of the processing workshop changes by 2 degrees. And judging whether the concentration of sulfide in the air changes along with the change of the temperature, if so, the temperature of the preset place is the reason for influencing the air deterioration of the preset place. If the temperature of the preset place is not changed, the temperature of the preset place is not a reason for influencing air deterioration of the preset place. When the air quality of the preset place can be influenced by the change of the temperature, the sulfide concentrations corresponding to different temperatures are correspondingly stored, and the temperature value when the sulfide concentration reaches the concentration influencing the air quality is recorded.
The calculation module 102 is configured to input the disaster prevention condition data and the loss condition data into a preset calculation model, and output disaster guarantee data required by the to-be-evaluated site in the disaster scene.
For example, in an embodiment of the present invention, the disaster insurance resource calculation method is applied to a premium calculation system of an insurance company. The disaster prevention condition data includes environmental condition information, article condition information, and personnel condition information in the location to be insured. The damage status data may be a damage status of the to-be-insured place in a fire, and the disaster securing resource is an amount of money to be insured for the to-be-insured place. And inputting the disaster prevention condition data and the loss condition data of the to-be-insured place into a preset calculation model, and outputting the premium amount required by the to-be-insured place in the disaster scene.
The training method of the preset calculation model comprises the following steps:
(1) acquiring disaster prevention condition data of different places, loss condition data of the places in disaster scenes and disaster guarantee resources required by the places in the disaster scenes, correspondingly storing the disaster prevention condition data of each place, the loss condition data of the places in the disaster scenes and the disaster guarantee resources required by the places in the disaster scenes, and dividing the disaster prevention condition data, the loss condition data and the disaster guarantee data of the places into a training set and a verification set.
Disaster guarantee resources required by the site in the disaster scene are disaster guarantee resources historically invested by the site, and the historically invested disaster guarantee resources are stored in the computer device 2.
For example, the computer device 2 is an electronic device for storing user data of an insurance company, and the electronic device stores policy information of a historical insurance application site, the policy information including disaster prevention condition data of the insurance application site, damage condition data of the disaster in which the site is present, which is simulated by a disaster numerical simulation system, and an application amount of the insurance application site. And dividing the data in the historical policy information into a training set and a verification set.
(2) Establishing a neural network-based calculation model, and training parameters of the calculation model by using the training set, wherein disaster prevention condition data and loss condition data in the training set are used as input data of the model, and the disaster guarantee data is used as output data of the model.
The neural network-based computing model comprises various algorithm structures and can comprise a convolutional neural network-based computing model, a genetic algorithm-based neural network, a fuzzy theory-based neural network and the like.
(3) And verifying the trained calculation model by using the verification set, and counting according to a verification result to obtain the model prediction accuracy.
And inputting the disaster prevention condition data in the verification set and the loss condition data of the site in the disaster scene into the calculation model, calculating disaster guarantee resources required by the site in the disaster scene, comparing the calculated disaster guarantee resources with the disaster guarantee resources in the training set, and verifying the prediction accuracy of the model according to the comparison result.
(4) And judging whether the model prediction accuracy is smaller than a preset threshold value or not.
In one embodiment, the prediction accuracy is 95%.
(5) And if the model prediction accuracy is not smaller than the preset threshold, finishing the training of the calculation model.
(6) And if the model prediction accuracy is smaller than the preset threshold, adjusting the structure of the calculation model, and retraining the adjusted calculation model by using the training set, wherein the structure of the calculation model comprises at least one of the number of convolution kernels, the number of elements in the pooling layer and the number of elements in the full connection layer.
(7) And verifying the retrained calculation model by using the verification set, carrying out statistics again according to verification results to obtain the model prediction accuracy of the adjusted calculation model, and judging whether the prediction accuracy of the adjusted calculation model is smaller than the preset threshold value.
(8) And if the model prediction accuracy obtained by the re-statistics is not less than the preset threshold, finishing the training of the calculation model.
(9) If the model prediction accuracy obtained by the re-statistics is smaller than the preset threshold, repeating the steps of adjusting and training until the model prediction accuracy obtained by the verification of the verification set is not smaller than the preset threshold.
The steps in the above-mentioned preset training mode of the calculation model may be changed according to the order of actually required steps, and some steps may be omitted. The training method can be completed on line or off line.
Example four
FIG. 4 is a diagram of a computer device according to a preferred embodiment of the present invention.
The computer device 1 comprises a memory 20, a processor 30 and a computer program 40, such as a disaster assurance resource calculation program, stored in the memory 20 and executable on the processor 30. The processor 30 implements the steps of the disaster securing resource calculating method in the embodiment, such as the steps S1 to S2 shown in fig. 2, when executing the computer program 40. Alternatively, the processor 30 executes the computer program 40 to implement the functions of the modules/units in the disaster insurance resource calculation apparatus embodiment, for example, the unit 101 and the unit 102 in fig. 3.
Illustratively, the computer program 40 may be partitioned into one or more modules/units that are stored in the memory 20 and executed by the processor 30 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, the instruction segments describing the execution process of the computer program 40 in the computer apparatus 1. For example, the computer program 40 may be divided into an acquisition module 101 and a calculation module 102 in fig. 3.
The computer device 1 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. It will be appreciated by a person skilled in the art that the schematic diagram is merely an example of the computer apparatus 1, and does not constitute a limitation of the computer apparatus 1, and may comprise more or less components than those shown, or some components may be combined, or different components, for example, the computer apparatus 1 may further comprise an input and output device, a network access device, a bus, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor 30 may be any conventional processor or the like, the processor 30 being the control center of the computer device 1, various interfaces and lines connecting the various parts of the overall computer device 1.
The memory 20 may be used for storing the computer program 40 and/or the module/unit, and the processor 30 implements various functions of the computer device 1 by running or executing the computer program and/or the module/unit stored in the memory 20 and calling data stored in the memory 20. The memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the computer apparatus 1, and the like. In addition, the memory 20 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The modules/units integrated with the computer device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the steps of the above-described embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
In the embodiments provided in the present invention, it should be understood that the disclosed computer apparatus and method can be implemented in other ways. For example, the above-described embodiments of the computer apparatus are merely illustrative, and for example, the division of the units is only one logical function division, and there may be other divisions when the actual implementation is performed.
In addition, functional units in the embodiments of the present invention may be integrated into the same processing unit, or each unit may exist alone physically, or two or more units are integrated into the same unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The units or computer means recited in the computer means claims may also be implemented by the same unit or computer means, either in software or in hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A disaster assurance resource calculation method, the method comprising:
acquiring disaster prevention condition data of a site to be evaluated and loss condition data of the site to be evaluated in a disaster scene, wherein items in the disaster prevention condition data comprise environment state information, article state information and personnel number;
and inputting the disaster prevention condition data and the loss condition data into a preset calculation model, and outputting disaster guarantee resources required by the to-be-evaluated site in the disaster scene.
2. The disaster assurance resource calculation method of claim 1, wherein the method of acquiring loss condition data of the site to be assessed in a disaster scene comprises:
acquiring disaster prevention condition data of a to-be-evaluated site, performing simulation under different disaster scenes on the to-be-evaluated site through a disaster numerical simulation system, and calculating loss condition data of the to-be-evaluated site under the different disaster scenes.
3. The disaster securing resource calculating method as claimed in claim 2, wherein the method further comprises:
simulating the disaster prevention condition data of the site to be evaluated by a disaster prediction simulation system, and judging whether a disaster is caused along with the change of the disaster prevention condition data;
the method for judging whether disasters can be caused comprises the following steps:
dividing the data into a plurality of sections according to the change range of the disaster prevention condition data of each place;
sequentially inputting the disaster prevention condition data of each place into a disaster prediction simulation system according to different variable quantities according to the change rule of the interval;
and if the change of the data can trigger a disaster-causing condition, determining that the data can cause the disaster, wherein the disaster-causing condition comprises temperature, dust content in the air and harmful gas concentration in the air.
4. The disaster assurance resource calculation method according to claim 2, wherein the method of performing simulation in different disaster scenarios on the site to be evaluated by the disaster numerical simulation system and calculating loss condition data of the site to be evaluated in the different disaster scenarios comprises:
setting the loss proportion of each object in the to-be-evaluated place in each disaster within unit time;
dividing the object according to a preset proportion, wherein each divided area represents the minimum amount of money lost by the object in a disaster within a unit time;
and calculating the loss condition data of the place in each disaster scene according to the loss proportion of the object in each disaster in unit time and the minimum amount lost in each disaster in unit time.
5. The disaster assurance resource calculation method of claim 1, wherein the training of the calculation model comprises:
acquiring disaster prevention condition data of different places, loss condition data of the places in disaster scenes and disaster guarantee resources required by the places in the disaster scenes, correspondingly storing the disaster prevention condition data of each place, the loss condition data of the places in the disaster scenes and the disaster guarantee resources required by the places in the disaster scenes, and dividing the disaster prevention condition data, the loss condition data and the disaster guarantee data of the places into a training set and a verification set;
establishing a neural network-based calculation model, and training parameters of the calculation model by using the training set, wherein disaster prevention condition data and loss condition data in the training set are used as input data of the model, and the disaster guarantee data is used as output data of the model;
verifying the trained calculation model by using the verification set, and counting according to a verification result to obtain the model prediction accuracy;
judging whether the model prediction accuracy is smaller than a preset threshold value or not;
and if the model prediction accuracy is not smaller than the preset threshold, finishing the training of the calculation model.
6. The disaster securing resource calculating method as claimed in claim 5, wherein the method further comprises:
if the model prediction accuracy is smaller than the preset threshold, adjusting the structure of the calculation model, and retraining the adjusted calculation model by using the training set, wherein the structure of the calculation model comprises at least one of the number of convolution kernels, the number of elements in the pooling layer and the number of elements in the full connection layer;
verifying the retrained calculation model by using the verification set, carrying out statistics again according to a verification result to obtain the model prediction accuracy of the adjusted calculation model, and judging whether the prediction accuracy of the adjusted calculation model is smaller than the preset threshold value or not;
if the model prediction accuracy obtained by the re-statistics is not smaller than the preset threshold, finishing the training of the calculation model;
if the model prediction accuracy obtained by the re-statistics is smaller than the preset threshold, repeating the steps of adjusting and training until the model prediction accuracy obtained by the verification of the verification set is not smaller than the preset threshold.
7. A disaster assurance resource computing apparatus, the apparatus comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring disaster prevention condition data of a place to be evaluated and loss condition data of the place in a disaster scene, and items in the disaster prevention condition data comprise environment state information, article state information and personnel number;
and the computing module is used for inputting the disaster prevention condition data and the loss condition data into a preset computing model and outputting disaster guarantee data required by the to-be-evaluated site in the disaster scene.
8. A computer device, characterized by: the computer arrangement comprises a processor for implementing the disaster guaranteed resource calculation method as claimed in any one of claims 1-6 when executing a computer program stored in a memory.
9. A computer storage medium having a computer program stored thereon, characterized in that: the computer program, when executed by a processor, implements a disaster guaranteed resource calculation method as defined in any one of claims 1-6.
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| US16/835,625 US11552978B2 (en) | 2019-12-30 | 2020-03-31 | Disaster security calculation method, and user terminal and non-transitory medium implementing same |
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| US9268797B2 (en) * | 2012-12-21 | 2016-02-23 | Zetta Inc. | Systems and methods for on-line backup and disaster recovery |
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| US11750633B2 (en) * | 2018-09-27 | 2023-09-05 | Riskq, Inc. | Digital asset based cyber risk algorithmic engine, integrated cyber risk methodology and automated cyber risk management system |
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